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All Journal International Journal of Applied Power Engineering (IJAPE) Bulletin of Electrical Engineering and Informatics
Kambhampati Venkata Govardhan Rao
St. Martin’s Engineering College
Electrical & Electronics Engineering

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Super-twisting sliding mode control for enhanced performance of grid-connected PV systems with H-bridge multilevel inverter CH. Venkata Amarnadh; T. Vijay Muni; T. Anuradha Devi; Rakesh Teerdala; M. Kiran Kumar; Kambhampati Venkata Govardhan Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp464-479

Abstract

This paper presents an enhanced control strategy for a grid-connected photovoltaic (PV) system employing a novel H-bridge multilevel inverter (MLI). The key contribution of this work lies in replacing the conventional proportional-integral (PI) controller with a super-twisting sliding mode controller (STSMC) for DC-link voltage regulation. Unlike earlier approaches that suffer from slow response, steady-state errors, and limited robustness under varying solar and temperature conditions, the proposed STSMC ensures faster transient response, finite-time convergence, and strong disturbance rejection without the chattering problem of classical sliding mode controllers. Another distinctive aspect of this study is the integration of STSMC with direct model predictive control (DMPC) for grid current regulation, enabling accurate reference current generation and improved synchronization. The novel H-bridge MLI topology further enhances system efficiency by reducing the number of switches while producing a seven-level output with lower total harmonic distortion (THD). Simulation results demonstrate that the proposed strategy achieves superior performance compared to the conventional PI-based system, with improvements in voltage stability, current quality, and reduced THD. These findings confirm the novelty and effectiveness of the proposed control scheme for reliable and efficient PV grid integration.
Empowering energy management: anomaly detection in smart meter data for proactive consumption control Batchalakuri Jyothi; Bhavana Pabbuleti; Beeravalli Mounika; Hrushitha Kalapala; Meda Uma Santhosh Chandra; Sanaboina Sai Srilakshmi; Bommasani Ganesh Babu; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Rami Reddy Chilakala
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10957

Abstract

The increasing deployment of smart energy meters (SEMs) has enabled real-time monitoring of energy consumption, but the vast data generated makes it challenging to detect anomalies that may indicate inefficiencies, faults, or unauthorized usage. This study aims to enhance energy management by developing a hybrid anomaly detection framework that improves accuracy while providing actionable insights for consumers. The proposed method integrates statistical and machine learning (ML) approaches, specifically Z-score, local outlier factor (LOF), one-class support vector machine (SVM), and isolation forest (iForest), to analyze simulated smart meter data. An anomaly is flagged only when identified by all four methods, thereby reducing false positives and improving reliability. The framework is implemented in an interactive dashboard built with streamlit, offering real-time visualization, peak-time alerts, usage forecasts, and personalized consumption suggestions. Results demonstrate that the hybrid approach outperforms single-method models, achieving higher detection accuracy and practical applicability. The findings highlight the potential of combining complementary detection techniques with proactive feedback to empower consumers, reduce energy wastage, and support sustainable energy management. This work provides a scalable foundation for future real-time deployment in smart grids and microgrid environments.
Smart charging of electric vehicles at a charging station using machine learning and pressure pad energy harvesting Kumara Swamy Tadi; Ganapaneni Swapna; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Srungaram Ravi Teja
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10635

Abstract

The rapid growth of electric vehicles (EVs) demands intelligent, cost-effective, and sustainable charging solutions. This paper introduces a smart EV charging station system that integrates machine learning (ML) with pressure pad–based energy harvesting. The system forecasts energy demand, predicts vehicle types and slot needs, and recommends optimal charging times using real-time data such as state of charge (SoC), battery health, and user behavior patterns. ML models such as long short-term memory (LSTM) and random forest are employed to ensure accurate scheduling and forecasting. A smart display, the display slot indicator (DSI), powered by sensors and station data, guides users with live cost, time, and slot availability, including alternate suggestions during peak demand. The pressure pad not only contributes to energy recovery but also aids in real-time vehicle detection and traffic regulation within the station. With scalable capacity and intelligent automation, this system can support more than 400 EVs per day, minimizing operational load and energy waste while maximizing convenience and sustainability.
Advanced artificial intelligence-based multi-sensor fusion for environmental perception in autonomous electric vehicles Billu Naveen; Malligunta Kiran Kumar; Thalanki Venkata Sai Kalyani; Thulasi Bikku; Kambhampati Venkata Govardhan Rao
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10739

Abstract

As autonomous electric vehicles (AEVs) continue to evolve, the demand for robust obstacle detection systems becomes increasingly critical to ensure safety, efficiency, and adaptability in real-world environments. This review presents a comprehensive synthesis of recent advancements in sensor fusion technologies, emphasizing the integration of light detection and ranging(LiDAR), radar, and camera-based vision systems. It highlights the role of deep learning architectures—such as you only look once (YOLO), convolutional neural networks (CNNs), and related neural models—in enhancing object detection, classification, and segmentation. The review categorizes key research themes, including fusion methodologies, real-time processing, edge computing, performance in adverse weather conditions, pedestrian detection, and sensor calibration. Special attention is paid to techniques that merge spatial, velocity, and semantic data to mitigate individual sensor limitations. The paper also discusses hardware-accelerated solutions for low-latency inference and the use of lightweight models for deployment on edge devices. Benchmark datasets, of vehicle-to-everything (V2X) and internet of thing (IoT)-based infrastructure, and calibration challenges are examined for their roles in ensuring accuracy and reliability. Drawing from over 100 referenced studies, this work serves as a foundational resource for researchers and developers aiming to advance artificial intelligence (AI)-based sensor fusion systems in next-generation AEVs.
Enhancing urban EV integration: a data-driven hybrid approach to charging station optimization and energy management Shaik Mohammed Hussain; Ganapaneni Swapna; Kambhampati Venkata Govardhan Rao; Malligunta Kiran Kumar; Srungaram Ravi Teja
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10614

Abstract

Electric vehicles (EVs) are pivotal to sustainable urban mobility, but their large-scale adoption in developing cities depends on efficient charging infrastructure and grid stability. This study proposes a hybrid deep learning framework to optimize EV charging station placement and energy scheduling in Vijayawada, India, projected to host 70,000 EVs by 2028. A convolutional neural network (CNN) is employed to classify charger types (Fast vs. Level 2) based on spatial features such as geospatial coordinates, population density, and traffic volume, while a long short-term memory (LSTM) network forecasts hourly charging demand using synthetic 24-hour sequences. The dataset comprises 108 candidate locations, designed to mirror real usage patterns. Model performance is evaluated using classification accuracy and mean absolute error (MAE). Results indicate that the CNN achieved 92% accuracy in charger type prediction, while the LSTM produced an hourly demand forecast with an MAE of 25 sessions/hour. These outcomes demonstrate the framework’s ability to reduce grid stress by shifting peak loads and strategically placing chargers in high-demand zones. The study provides a scalable and adaptable solution for EV infrastructure planning, enabling resilient grid integration, and supporting sustainable urban energy systems.
Co-Authors Batchalakuri Jyothi Beeravalli Mounika Bhavana Pabbuleti Billu Naveen Bommasani Ganesh Babu CH. Venkata Amarnadh Ganapaneni Swapna Hrushitha Kalapala Kumara Swamy Tadi M. Kiran Kumar Malligunta Kiran Kumar Meda Uma Santhosh Chandra Rakesh Teerdala Rami Reddy Chilakala Sanaboina Sai Srilakshmi Shaik Mohammed Hussain Srungaram Ravi Teja T. Anuradha Devi T. Vijay Muni Thalanki Venkata Sai Kalyani Thulasi Bikku